Direct conversion of fibroblasts into motor neurons via the forced overexpression of transcription factors will enable both the study of neurodegenerative diseases and the high- throughput identification of therapeutics. Induced motor neurons (iMNs) converted from fibroblasts isolated from patients suffering from amyotrophic lateral sclerosis (ALS) recapitulate neurodegenerative processes in vitro. By providing a disease in a dish, iMNs facilitate the study of the mechanisms of neurodegeneration and allow for the relatively rapid screening and identification of therapeutic compounds. However, accurately modeling neurological disorders relies on robust, reliable methods that efficiently generate the distinct neural subpopulations affected by the disease. Thus far, conversion remains inefficient, and the central mechanistic rules for direct lineage conversion remain undefined. Further, many cellular properties that are relevant for both research and therapeutic applications are uncharacterized. Finally, the question of how the starting population of fibroblasts influences the conversion, neuronal identity, and maturation of these neurons stands unexplored. Single-cell transcriptional profiling of iMNs will enable us to refine our understanding of the coordinated patterns of expression that are unique to particular subpopulations of cells. Single-cell profiling resolves bias introduced by bulk profiling, in which only the population average is observed, allowing us to more reliably identify transcriptional determinants of fate. Using single-cell transcriptional profiling, we aim to generate an improved mechanistic understanding of the molecular processes and cellular states that direct the conversion of fibroblasts into iMNs. Further, we will harness the identified transcriptional determinates of fate into synthetic circuits to enhance conversion efficiency and neuron maturity. Identifying optimal transcriptional profiles and the molecular rules of conversion will enable the construction of sophisticated synthetic circuits that drive cells toward desired fates. Single-cell transcriptional profiling data may suggest general mechanisms that influence conversion and may provide an explanation of how cells process complex sets of cellular cues into robust, long-term cellular identities. By understanding the molecular mechanisms of conversion, we can construct enhanced reprogramming strategies and potentially illuminate general principles for the direct conversion of a variety of cell types, enabling a broad range of disease models for therapeutic screens and the replacement of diseased and damaged tissues.